Segmentation of Radar images by combining watershed and Fisher techniques for target

classification

B.BELKHAOUI1, 2, A.TOUMI2, A.KHALFALLAH1, A. KHENCHAF2 and M.S.BOUHLEL1 1Unité de Recherche : Sciences Et Technologies de l'Image et des Télécommunications (SETIT)

ISBS, université de Sfax, Tunisie 2Laboratoire -sticc UMR CNRS 6285

ENSTA Bretagne, Brest, France bannour.belkhaoui@ensta-bretagne.fr, toumiab@ensta-bretagne.fr, ali.khalfallah@isecs.rnu.tn, ali.khenchaf@ensta-bretagne.fr

and medsalim.bouhlel@enis.rnu.tn

Abstract—In the area of monitoring and observation by sea, land or air by the use of information is an important task for the decision. We are interested in the present work on the information processing for the recognition of radar targets from ISAR images. To provide the information necessary for the recognition phase, we propose an approach radar image processing based on the combination algorithms Fisher and watershed. This combination allows us to obtain closed shapes (edge). Then, we proceed to a shape modeling based on the Fourier descriptor. Finally, we use the KNN classifier to evaluate the performance of our approach. The obtained results seem good.

Keywords—Images radar ISAR; Fisher; Watershed; KNN classification; Fourier descriptor

I. INTRODUCTION The image acquisition and identification of objects for

humans takes place through the visual system that is superior in terms of performance than all artificial systems. This method is based primarily on a brain processing of information captured by the eye. However, the eye has limitations mainly face the darkness, often explained by its sensitivity to certain wavelengths. In order to circumvent these limitations, the man had recourse to science by taking advantage of technological advancements of sensors (e.g. radar) through the electronic imaging. In this context, the research has focused on systems that collect information such as thermography and radiography to compensate the limitations of traditional image capture system (CCD). Indeed, radar images (Radio Detection And Ranging) are obtained by reflection of the radio waves to detect the position and velocity of a target based on the time and the frequency of the reflected signal. This imaging system is used in several areas: civil, military [6], mapping [8], oceanography [7], Glaciology [2], etc…. However, these systems of data collection and imaging have limitations that differ from one system to another. Better exploitation of the image requires special treatment according to the application.

In this work, we will focus on the recognition of radar targets by exploiting the ISAR images. The proposed approach is based on on a combination of Fisher's algorithm and the algorithm of watershed lines (WS) to ensure edge detection. Then, we proceed to a shape modeling by a Fourier descriptor invariant to geometric changes. To validate the obtained descriptors, a classification results by K-nearest neighbors will be presented.

In what follows, we will detail the different techniques used in our approach. Then, we will outline the results while highlighting the effect of the number of neighbors and the size of training set adopted in the classification phase of the target images.

II. PROPOSED METHODE A collection of information in radar application consists of

five phases from acquisition and data preparation (pre-treatment and processing of data) to the interpretation and evaluation of results; through the phase data mining (data mining) the core of recognition / identification function of radar targets [12]. In Fig. 1, we illustrate the principle of our approach for the classification of radar ISAR images.

At first, we proceed to the contour extraction of the image. This phase is realized in two steps. Firstly, we proceed to an image thresholding by Fisher algorithm. Then, we apply the watershed algorithm on the resulting image to extract the general shape of the target.

Secondly, the closed obtained edge was converted to complex plane. Next, we have used modified Fourier descriptors to ensure invariance to geometrical changes (changes of scale, rotation and translation) for shape modeling.

Finally, we used the technical of k-nearest neighbors to classify targets illustrated in radar ISAR images.

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Fig. 1. flowchart of the proposed method

A. Image segmentation by algorithm Fisher The result of image segmentation depends heavily on the criterion chosen to define the thresholds separating the different classes. In this context, Fisher [5] proposed a technique to ensure optimal distribution of different individuals in a population characterized by a single variable [3]. This proposal seems adaptive in image processing considering the pixels as individuals characterized by their grayscale. Thus, the distribution of grayscale of an image into N classes goes through setting thresholds that separate them. Consequently, we define ti, mi and vi represent respectively the size, the average and the variance of a class Ci as follows:

( )2. ( ). ( )( ) , et i i i

Dj D j D ji i

H jj H j iH jj m

t m t tν= = =−

∑ ∑ ∑ (1)

With H is standardized histogram of the image and Dj = [si-1, si[the domain of grayscale associated with class Ci. According to Fisher algorithm, an optimal distribution is obtained by minimizing the total intra class variance W defined by the (2).

.i i

iW t ν= ∑

(2)

It should be noted that the algorithm of Fisher is not easily applicable to partitioning a distribution to a high number of classes. However, this algorithm is very effective for binary distribution as in our case.

B. Algorithm watershed The watershed (WS) is proposed for the first time by

Digabel and Lantuéjoul [4] by using this transformation of the basins slopes as morphological tool for the binary images. Further works aimed to generalize this segmentation technique for grayscale images [1] [10]. In this technique, the image is represented as a topographic surface where pixel coordinates are used to locate a pixel in Cartesian space while the pixel value represents the altitude of a geographic area. Thus, we can define the watershed limit as the peak separating two basin slopes [9] [13] [14] as illustrated in Fig. 2.

Fig. 2. Principle of watershed line

C. Shape Modeling (Fourier descriptor) The Fourier descriptor gives a frequency representation of the contour of the image extracted in the previous step [11]. In fact, we apply the discreet Fourier transform (DFT) to the extracted (represented in complex plane) edge of the radar ISAR image. The following equation describes the DFT:

12 * /

0

1( ) ( )N

j n k N

kF n z k

N e−

− ∏

=

= ∑ (3)

These descriptions are the signature of the object extracted from the image. This signature is computed for a fixed number n of complex coordinates of the extracted contour. This yields to a descriptor formed by n/2 coefficients.

Indeed, we have normalized the extracted contours to get the same size of extracted contours and thus the same size of the signature used in the classification phase.

Moreover, the Fourier descriptors calculated must be invariant to geometric changes (translation, rotation and / or scaling) to increase the performance of the classifier [12].

The translation of descriptors is integrated in the first coefficient F(0). To ensure translation invariance, we omit this first term of the Fourier descriptor.

Other hand, the Fourier descriptors of z '(k) = α .z (k), where, α is the factor scale:

( )

( )

12 * /' '

01

2 * /

0

1( ) ( )

1

Nj n k N

kN

j n k N

k

F n z kN

z kN

F n

e

eα

α

−− ∏

=

−− ∏

=

=

=

=

∑

∑

(4)

Therefore, to ensure scaling invariance, we divide the Fourier descriptor by its first coefficient (F (1) in our case).

Finally, the rotation by an angel θ is obtained by

multiplying the descriptors by the valueje θ

. Thus, this transformation affects only the Fourier descriptor phase. Accordingly, we use only the amplitudes of the Fourier descriptor coefficients.

D. Classification by KNN The In artificial intelligence, the method of K Nearest

Neighbors is a supervised learning method (Abbreviated k-NN or KNN).

In this context, it has a learning database consists of N pairs "input-output". To estimate the output associated with a new input x, the method of k nearest neighbors is taken into account (the same way) the k training samples whose entrance is closest

Original image

Edge extraction: Fisher and WS

Descriptor Classification

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to the new input x, according to distance to be defined. In a classification problem, we retain the class most represented among the k outputs associated with inputs k closest to the new input x.

III. RESULTS AND DISCUTION At first, we begin by illustrating the results of segmentation

and shape modeling obtained on two samples Fig. 3, of our bank ISAR radar image in grayscale. This process is applied to image bank.

Fig. 3. Example of ISAR images

Fig. 4, illustrates the results of Fisher thresholding applied on our two samples of grayscale radar ISAR images. Indeed, we apply the Fisher algorithm to separate the image in two classes: background and target. We note that this segmentation doesn’t permit us to have closed regions. Therefore, we cannot adopt it in our process of target recognition.

Fig. 4. Binary images (Fisher algorithm number of classes = 2).

Then, we apply the morphological gradient to the obtained binary images. This process aims to extracting the edge of target. The given results Fig. 5 show the presence of artifact which makes difficult the process of recognition.

Fig. 5. Morphological gradient of binary images

According to the literature, the application of the watershed algorithm to the gradient of the image provides closed contours. Therefore, we applied WS algorithm to last results. The obtained results show Fig. 6, many closed edges which represent the basin slopes of the two sample images.

Fig. 6. WS of gradient of the binary image

To reduce the number of edges, we suggest applying the morphological operators on the watershed image. The obtained result presents the extracted shape of our target Fig. 7.

Fig. 7. Extracted form of image ISAR

After that, we proceed to the shape modeling phase. So, we apply the discrete Fourier transform to the coordinated (complex plane) of extracted shape. Then, we compute, the Fourier descriptor of the target.

As mentioned at the beginning, we apply previous steps to all images of our image database. The database is composed of 648 ISAR images representing four targets. Each target is represented by 162 images where each image corresponds to a given viewing angle. In the classification phase this database is divided into two sub-bases: training base and test base.

Fig. 8, shows the rates of a good classification according to the k value adopted by the KNN classifier for different ratios (30%, 50%, and 70%) used to form the training base. We note that we have used the Euclidean distance as likeness metric in this phase.

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Fig. 8. Classification result according to the number of neighbors and the

size of the training base The obtained results prove a good performance of our

proposed method of target recognition. On the other hand, we conclude that adopting a training base constituted by 50% of the database ensure the best performance for the classifying process. Moreover, adopting 4 nearest neighbors by the KNN with a training base formed from 50% of the images radar ISAR database provides the highest good classification rate.

IV. CONCLUSION This work is interested in the classification of ISAR radar

images. In a first phase, we presented our approach for shape extracting of the target. This approach combines the algorithm of Fisher and watershed algorithm.

From, of this contour, the modeling of the shape is performed by calculating the Fourier descriptors. Finally, we applied the KNN algorithm for classifying targets. We evaluated our approach for different sizes of the training base and different numbers of K-nearest neighbor considered.

The results of this work can be used in future work with other image descriptors which could be useful to detect the presence of targets in noisy images. To improve the recognition rate of the system, other techniques for classification can be used.

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